TY - GEN
T1 - Fairness in representation
T2 - 19th SIAM International Conference on Data Mining, SDM 2019
AU - Abbasi, Mohsen
AU - Friedler, Sorelle A.
AU - Scheidegger, Carlos
AU - Venkatasubramanian, Suresh
N1 - Funding Information:
∗This research was funded in part by the NSF under grants IIS-1633387, IIS-1633724, IIS-1513651 and IIS-1815238. †University of Utah,mailto:mohsena@cs.utah.edu ‡Haverford College, mailto:sorelle@cs.haverford.edu §University of Arizona, mailto:cscheid@cs.arizona.edu ¶University of Utah, mailto:suresh@cs.utah.edu
Publisher Copyright:
Copyright © 2019 by SIAM.
PY - 2019
Y1 - 2019
N2 - While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. We also propose mitigation strategies and demonstrate their effectiveness on synthetic datasets.
AB - While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. We also propose mitigation strategies and demonstrate their effectiveness on synthetic datasets.
UR - http://www.scopus.com/inward/record.url?scp=85066080818&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066080818&partnerID=8YFLogxK
U2 - 10.1137/1.9781611975673.90
DO - 10.1137/1.9781611975673.90
M3 - Conference contribution
AN - SCOPUS:85066080818
T3 - SIAM International Conference on Data Mining, SDM 2019
SP - 801
EP - 809
BT - SIAM International Conference on Data Mining, SDM 2019
PB - Society for Industrial and Applied Mathematics Publications
Y2 - 2 May 2019 through 4 May 2019
ER -